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    {
      "name": "relu_1x1_sgmat_bias_z_c32_hw64",
      "provenance": { "source": "generated reference values", "test": "sgmat-eligible FusedConv epilogue" },
      "attrs": { "activation": "Relu" },
      "tolerance": 0.001,
      "relTolerance": 0.001,
      "inputs": {
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          "shape": [1, 32, 8, 8],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_1x1_sgmat_c32_hw64" } }
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            "values": { "$ref": "#/fixtureArrays/relu_1x1_sgmat_bias_z_c32_hw64_input_bias" }
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            "values": { "$ref": "#/fixtureArrays/input_z_residual_relu_1x1_sgmat_bias_z_c32_hw64" }
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    {
      "name": "relu_1x1_sgmat_bias_c32_hw64",
      "provenance": { "source": "generated reference values", "test": "sgmat-eligible FusedConv epilogue" },
      "attrs": { "activation": "Relu" },
      "tolerance": 0.001,
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      "inputs": {
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          "shape": [1, 32, 8, 8],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_1x1_sgmat_c32_hw64" } }
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            "values": { "$ref": "#/fixtureArrays/relu_1x1_sgmat_bias_z_c32_hw64_input_bias" }
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    {
      "name": "relu_1x1_sgmat_z_c32_hw64",
      "provenance": { "source": "generated reference values", "test": "sgmat-eligible FusedConv epilogue" },
      "attrs": { "activation": "Relu" },
      "tolerance": 0.001,
      "relTolerance": 0.001,
      "inputs": {
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          "shape": [1, 32, 8, 8],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_1x1_sgmat_c32_hw64" } }
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          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_w_relu_1x1_sgmat_c32_hw64" } }
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            "values": { "$ref": "#/fixtureArrays/input_z_residual_relu_1x1_sgmat_bias_z_c32_hw64" }
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    },
    {
      "name": "residual_1x1_sgmat_no_activation_c32_hw64",
      "provenance": {
        "notes": "Pins the residual-only cooperative epilogue on the subgroup-matrix 1x1 route. The residual is present while the activation attribute is omitted."
      },
      "tolerance": 0.001,
      "relTolerance": 0.001,
      "inputs": {
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          "dtype": "float32",
          "shape": [1, 32, 8, 8],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_1x1_sgmat_c32_hw64" } }
        },
        "w": {
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          "shape": [32, 32, 1, 1],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_w_relu_1x1_sgmat_c32_hw64" } }
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        "zResidual": {
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            "values": { "$ref": "#/fixtureArrays/input_z_residual_relu_1x1_sgmat_bias_z_c32_hw64" }
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      "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 8, 8], "tolerance": 0.001, "relTolerance": 0.001 } }
    },
    {
      "name": "relu_3x3_im2col_sgmat_c32",
      "provenance": { "source": "generated reference values", "test": "sgmat-eligible FusedConv epilogue" },
      "attrs": { "activation": "Relu" },
      "tolerance": 0.001,
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          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_3x3_im2col_sgmat_c32" } }
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          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_w_relu_3x3_im2col_sgmat_c32" } }
        }
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      "outputs": {
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      }
    },
    {
      "name": "relu_3x3_im2col_padded_c16",
      "provenance": { "source": "generated reference values", "test": "sgmat-eligible FusedConv epilogue" },
      "attrs": { "activation": "Relu" },
      "tolerance": 0.001,
      "relTolerance": 0.001,
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 16, 10, 10],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_x_relu_3x3_im2col_padded_c16" } }
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          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/input_w_relu_3x3_im2col_padded_c16" } }
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        "notes": "FusedConv inherits Conv auto_pad semantics. SAME_UPPER with stride 2 requires trailing padding and a 2x2 output here, exercising padding derived from the official auto_pad and strides attributes."
      },
      "attrs": { "auto_pad": "SAME_UPPER", "activation": "Relu", "strides": [2, 2] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 1, 4, 4],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/same_upper_stride2_autopad_input_x" } }
        },
        "w": { "dtype": "float32", "shape": [1, 1, 3, 3], "data": { "kind": "constant", "value": 1.0 } }
      },
      "outputs": {
        "y": {
          "dtype": "float32",
          "shape": [1, 1, 2, 2],
          "data": { "kind": "values", "values": [54.0, 45.0, 72.0, 54.0] },
          "tolerance": 0.000001
        }
      }
    },
    {
      "name": "conv1d_relu_direct",
      "provenance": {
        "source": "ONNX Runtime com.microsoft.FusedConv (CPUExecutionProvider)",
        "notes": "FusedConv has the same N-dimensional convolution schema as Conv. This compact rank-3 NCW case is accepted by ORT and evaluates Relu(Conv1D(X,W)) on the direct 1-D fallback."
      },
      "attrs": { "activation": "Relu" },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 1, 6],
          "data": { "kind": "values", "values": [-1.0, 2.0, 3.0, -4.0, 5.0, 6.0] }
        },
        "w": { "dtype": "float32", "shape": [1, 1, 3], "data": { "kind": "values", "values": [1.0, -1.0, 0.5] } }
      },
      "outputs": {
        "y": {
          "dtype": "float32",
          "shape": [1, 1, 4],
          "data": { "kind": "values", "values": [0.0, 0.0, 9.5, 0.0] },
          "tolerance": 0.000001
        }
      }
    },
    {
      "name": "conv3d_relu",
      "provenance": {
        "source": "com.microsoft FusedConv contract and ONNX Conv volumetric semantics",
        "notes": "FusedConv is defined over N-dimensional convolution. The generic direct kernel already emits a 3D loop nest for ai.onnx.Conv; this is the same nest with the fused epilogue on top. The trusted reference evaluates it as Relu(Conv3D(X,W))."
      },
      "attrs": { "activation": "Relu", "strides": [1, 1, 1], "dilations": [2, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 1, 4, 2, 2],
          "data": {
            "kind": "values",
            "values": [-1.0, 2.0, -3.0, 4.0, 5.0, -6.0, 7.0, -8.0, 9.0, 10.0, -11.0, 12.0, 13.0, -14.0, 15.0, 16.0]
          }
        },
        "w": { "dtype": "float32", "shape": [1, 1, 2, 1, 1], "data": { "kind": "values", "values": [1.0, -0.5] } }
      },
      "outputs": {
        "y": {
          "dtype": "float32",
          "shape": [1, 1, 2, 2, 2],
          "data": { "kind": "values", "values": [0.0, 0.0, 2.5, 0.0, 0.0, 1.0, 0.0, 0.0] },
          "tolerance": 0.000001
        }
      }
    },
    {
      "name": "conv3d_bias_leakyrelu_front_pad",
      "provenance": {
        "notes": "Volumetric bias + LeakyRelu with an asymmetric depth pad, so the fused epilogue and the front-pad guard of the 3D loop nest run together. The weights and bias are chosen so the pre-activation output straddles zero: with an all-positive output LeakyRelu is the identity and the fixture would not see the activation at all."
      },
      "attrs": {
        "activation": "LeakyRelu",
        "activation_params": [0.1],
        "strides": [1, 1, 1],
        "dilations": [1, 1, 1],
        "pads": [1, 0, 0, 0, 0, 0]
      },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 1, 3, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.41, "scale": 1.5 }
        },
        "w": { "dtype": "float32", "shape": [1, 1, 2, 1, 1], "data": { "kind": "values", "values": [1.0, -0.8] } },
        "bias": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [-0.3] } }
      },
      "outputs": {
        "y": { "dtype": "float32", "shape": [1, 1, 3, 2, 2], "tolerance": 0.000001, "relTolerance": 0.00001 }
      }
    },
    {
      "name": "conv3d_residual_relu",
      "provenance": {
        "notes": "Volumetric residual: Y = Relu(Conv3D(X,W) + Z). Z is Y-shaped, which is what let the rank-4 shape check on the residual become a plain sameShape."
      },
      "attrs": { "activation": "Relu", "strides": [1, 1, 1], "dilations": [1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 2, 3, 3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29, "scale": 1.2 }
        },
        "w": {
          "dtype": "float32",
          "shape": [2, 2, 2, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.13, "scale": 0.4 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 2, 2, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.53, "cosStep": 0.07, "scale": 0.8 }
        }
      },
      "outputs": {
        "y": { "dtype": "float32", "shape": [1, 2, 2, 2, 2], "tolerance": 0.000001, "relTolerance": 0.00001 }
      }
    },
    {
      "name": "conv3d_grouped_no_activation",
      "provenance": {
        "notes": "Grouped volumetric convolution with no activation, so the new per-group channel derives are exercised at rank 5 and the epilogue renders to the plain store."
      },
      "attrs": { "group": 2, "strides": [1, 1, 1], "dilations": [1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 4, 3, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.37, "scale": 1.1 }
        },
        "w": {
          "dtype": "float32",
          "shape": [4, 2, 2, 1, 1],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.23, "scale": 0.6 }
        }
      },
      "outputs": {
        "y": { "dtype": "float32", "shape": [1, 4, 2, 2, 2], "tolerance": 0.000001, "relTolerance": 0.00001 }
      }
    },
    {
      "name": "conv3d_bias_residual_sigmoid",
      "provenance": {
        "notes": "The fourth corner of the volumetric family: bias and residual together, under a Sigmoid whose output is bounded away from the linear region so the epilogue order (conv + B + Z, then activate) is visible."
      },
      "attrs": { "activation": "Sigmoid", "strides": [1, 1, 1], "dilations": [1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 2, 3, 3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 1.3 }
        },
        "w": {
          "dtype": "float32",
          "shape": [2, 2, 2, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.5 }
        },
        "bias": {
          "dtype": "float32",
          "shape": [2],
          "data": { "kind": "fillFloat32", "sinStep": 0.7, "cosStep": 0.3, "scale": 1.0 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 2, 2, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.47, "cosStep": 0.13, "scale": 0.9 }
        }
      },
      "outputs": {
        "y": { "dtype": "float32", "shape": [1, 2, 2, 2, 2], "tolerance": 0.000001, "relTolerance": 0.00001 }
      }
    },
    {
      "name": "hardswish_conv1d_tiled_k3_s1_p1_bias_c16m128_w1024",
      "provenance": {
        "source": "onnxruntime/contrib_ops/cpu/fused_activation.cc",
        "notes": "Large NCW convolution with bias and provider-backed HardSwish on the register-tiled implicit-GEMM path."
      },
      "attrs": { "activation": "HardSwish", "strides": [1], "pads": [1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 16, 1024],
          "data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.013, "cosStep": 0.029 }
        },
        "w": {
          "dtype": "float32",
          "shape": [128, 16, 3],
          "data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.019, "cosStep": 0.007 }
        },
        "bias": {
          "dtype": "float32",
          "shape": [128],
          "data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.023, "cosStep": 0.017 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 1024], "tolerance": 0.0001, "relTolerance": 0.0001 } }
    },
    {
      "name": "hardswish_conv1d_tiled_k3_s2_p1_c16m128_w2047",
      "provenance": {
        "source": "onnxruntime/contrib_ops/cpu/fused_activation.cc",
        "notes": "Strided NCW convolution over an odd input width with provider-backed HardSwish and no bias; covers the unbiased register-tiled path."
      },
      "attrs": { "activation": "HardSwish", "strides": [2], "pads": [1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 16, 2047],
          "data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.017, "cosStep": 0.023 }
        },
        "w": {
          "dtype": "float32",
          "shape": [128, 16, 3],
          "data": { "kind": "fillFloat32", "scale": 0.25, "sinStep": 0.011, "cosStep": 0.031 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 1024], "tolerance": 0.0001, "relTolerance": 0.0001 } }
    },
    {
      "name": "relu_2x2_im2col_sgmat_z_c8m32",
      "provenance": {
        "notes": "Non-1x1 kernel keeps the 1x1 subgroup-matrix shortcut out, so the exact (unpadded) im2col subgroup-matrix GEMM owns the z-residual epilogue. 2x2 over 8 channels lands kernelRows=32 and outputPixels=64, both on the alignment the exact path requires."
      },
      "attrs": { "activation": "Relu", "strides": [1, 1] },
      "tolerance": 0.002,
      "relTolerance": 0.002,
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 8, 9, 9],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29, "scale": 0.3, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [32, 8, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19, "scale": 0.05, "offset": 0.0 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 32, 8, 8],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11, "scale": 0.2, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 8, 8] } }
    },
    {
      "name": "relu_2x2_im2col_sgmat_bias_c8m32",
      "provenance": {
        "notes": "Biased twin of relu_2x2_im2col_sgmat_z_c8m32 without the residual: a non-1x1 kernel on the exact im2col alignment (kernelRows=32, outputPixels=64) is the only way the biased exact subgroup-matrix GEMM outranks the 1x1 shortcut."
      },
      "attrs": { "activation": "Relu", "strides": [1, 1] },
      "tolerance": 0.002,
      "relTolerance": 0.002,
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 8, 9, 9],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29, "scale": 0.3, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [32, 8, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19, "scale": 0.05, "offset": 0.0 }
        },
        "bias": {
          "dtype": "float32",
          "shape": [32],
          "data": { "kind": "fillFloat32", "sinStep": 0.03, "cosStep": 0.17, "scale": 0.1, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 8, 8] } }
    },
    {
      "name": "relu_2x2_im2col_padded_z_n49_c8m32",
      "provenance": {
        "notes": "outputPixels=49 breaks the %64 N alignment the exact im2col subgroup-matrix path demands, so the zero-padded subgroup-matrix GEMM (N padded 49 -> 64) takes the no-bias z-residual route."
      },
      "attrs": { "activation": "Relu", "strides": [1, 1] },
      "tolerance": 0.002,
      "relTolerance": 0.002,
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 8, 8, 8],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.29, "scale": 0.3, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [32, 8, 2, 2],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.19, "scale": 0.05, "offset": 0.0 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 32, 7, 7],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11, "scale": 0.2, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 7, 7] } }
    },
    {
      "name": "group2_3x3_direct_z_inchan4_4x4",
      "provenance": {
        "notes": "group != 1 fails the dense-group contract every im2col/1x1 GEMM route requires, leaving the direct unrolled kernel to carry the z-residual epilogue."
      },
      "attrs": { "group": 2, "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 4, 4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.5, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [4, 2, 3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.1, "offset": 0.0 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 4, 4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11, "scale": 0.2, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 4, 4] } }
    },
    {
      "name": "group2_3x3_direct_bias_inchan4_4x4",
      "provenance": {
        "notes": "Grouped twin of group2_3x3_direct_z_inchan4_4x4 with a bias and no residual: pins the biased direct unrolled kernel, unreachable while every biased fixture is dense-group."
      },
      "attrs": { "group": 2, "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 4, 4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.5, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [4, 2, 3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.1, "offset": 0.0 }
        },
        "bias": {
          "dtype": "float32",
          "shape": [4],
          "data": { "kind": "fillFloat32", "sinStep": 0.03, "cosStep": 0.17, "scale": 0.1, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 4, 4] } }
    },
    {
      "name": "group2_3x3_direct_bias_z_inchan4_4x4",
      "provenance": {
        "notes": "Grouped conv carrying both a bias and a z-residual: pins the direct unrolled kernel's full epilogue (bias then residual then activation)."
      },
      "attrs": { "group": 2, "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 4, 4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.5, "offset": 0.0 }
        },
        "w": {
          "dtype": "float32",
          "shape": [4, 2, 3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.1, "offset": 0.0 }
        },
        "bias": {
          "dtype": "float32",
          "shape": [4],
          "data": { "kind": "fillFloat32", "sinStep": 0.03, "cosStep": 0.17, "scale": 0.1, "offset": 0.0 }
        },
        "zResidual": {
          "dtype": "float32",
          "shape": [1, 4, 4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11, "scale": 0.2, "offset": 0.0 }
        }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 4, 4, 4] } }
    },
    {
      "name": "same_lower_stride2_autopad",
      "provenance": {
        "source": "ONNX Runtime com.microsoft.FusedConv CPUExecutionProvider",
        "notes": "ONNX auto_pad SAME_LOWER puts the odd padding element at the START of the axis, so unlike its SAME_UPPER twin this case has a non-zero leading pad and fails if the kernel is handed the explicit pad attributes instead of the derived ones."
      },
      "attrs": { "auto_pad": "SAME_LOWER", "activation": "Relu", "strides": [2, 2] },
      "inputs": {
        "x": {
          "dtype": "float32",
          "shape": [1, 1, 4, 4],
          "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/same_upper_stride2_autopad_input_x" } }
        },
        "w": { "dtype": "float32", "shape": [1, 1, 3, 3], "data": { "kind": "constant", "value": 1.0 } }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 2], "tolerance": 0.000001 } }
    },
    {
      "name": "grouped_k5_relu_w4_group4",
      "provenance": {
        "source": "authored for route coverage",
        "notes": "Grouped 5x5 with a fused Relu on the wide-output kernel FusedConv had no grouped route to reach at all; every grouped shape went to direct_unrolled."
      },
      "attrs": { "activation": "Relu", "group": 4, "strides": [1, 1], "pads": [2, 2, 2, 2] },
      "tolerance": 0.0002,
      "relTolerance": 0.0002,
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 64, 16, 20] },
        "w": { "dtype": "float32", "shape": [64, 16, 5, 5] }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 16, 20] } }
    },
    {
      "name": "grouped_k5_relu_w4_group4_odd_width_tail",
      "provenance": {
        "source": "authored for route coverage",
        "notes": "The same grouped shape with a width that is not a multiple of four, covering the odd-width tail arm with the activation applied."
      },
      "attrs": { "activation": "Relu", "group": 4, "strides": [1, 1], "pads": [2, 2, 2, 2] },
      "tolerance": 0.0002,
      "relTolerance": 0.0002,
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 64, 16, 17] },
        "w": { "dtype": "float32", "shape": [64, 16, 5, 5] }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 16, 17] } }
    },
    {
      "name": "grouped_k3_hardswish_depthwise",
      "provenance": {
        "source": "authored for route coverage",
        "notes": "Depthwise 3x3 with a fused HardSwish, exercising the non-Relu branch of the activation partial on the grouped kernel."
      },
      "attrs": { "activation": "HardSwish", "group": 32, "strides": [1, 1], "pads": [1, 1, 1, 1] },
      "tolerance": 0.0002,
      "relTolerance": 0.0002,
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 32, 16, 20] },
        "w": { "dtype": "float32", "shape": [32, 1, 3, 3] }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 16, 20] } }
    }
  ]
}